REVIEW ARTICLE
Research on the Prediction Model of Material Cost Based on Data Mining
Liu Shenyang*, 1, Gao Qi1, Li Zhen1, Li Si1, Li Zhiwei1, 2
1 Department of Equipment Command & Management of Mechanical Engineering College, Shijiazhuang, Hebei,
050003, P.R. China
Article Information
Identifiers and Pagination:
Year: 2015Volume: 9
First Page: 1062
Last Page: 1066
Publisher Id: TOMEJ-9-1062
DOI: 10.2174/1874155X01509011062
Article History:
Received Date: 17/02/2014Revision Received Date: 21/03/2015
Acceptance Date: 09/06/2015
Electronic publication date: 28/10/2015
Collection year: 2015
© 2015 Shenyang et al
open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: (https://creativecommons.org/licenses/by/4.0/legalcode). This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: (https://creativecommons.org/licenses/by/4.0/legalcode). This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Abstract
Material cost prediction should be based on the scientific mathematical models so that the influence of subjective factors on the quota and other indicators of decomposition can be reduced. This paper analyzes the particle swarm optimization (PSO) algorithm to optimize the parameters of support vector machine and establishes the prediction model of material cost after preprocessing the actual data and uses the support vector regression (SVR) machine to carry out data mining. In the forecasting process, the total cost of material is first predicted and the predicted results are then adjusted with the actual value, and finally, the relative errors are tested. The result indicates that the forecasting effect is fulfilled.
Keywords: Data mining, optimization algorithm, particle swarm prediction of material cost, support vector machine.
